The fact that muscles are composed of different Motor Units (MUs) is often neglected when investigating motor control by macro models of human musculo-skeletal-joint systems. Each muscle is associated with one control...
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The fact that muscles are composed of different Motor Units (MUs) is often neglected when investigating motor control by macro models of human musculo-skeletal-joint systems. Each muscle is associated with one control signal. This simplification leads to difficulties when mechanical and electrical manifestations of the muscle activity are juxtaposed. That is why a new approach for muscle modelling was recently proposed (Journal of Biomechanics 2002;35:1123-1135). It is based on MUs twitches and a hierarchical genetic algorithm (HGA) is implemented for choosing the moments of activation of the individual MUs, thus simulating the control of the nervous system. Its basic benefit is obtaining the complete information about the mechanical and activation behaviour of all MUs, respectively muscles, during the whole motion. Its possibilities are demonstrated when simulating fast elbow flexion. Three flexor and two extensor muscles, each consisting of approximately real number of different types of MUs, are modelled. The task is highly indeterminate and the optimization is performed according to a fitness function that is an assessed combination of criteria (minimal deviation from the given joint moment, minimal total muscle force and minimal MUs activation). The influence of the weight of the first criterion (the one that reflects the importance of the movement accuracy on the predicted results), is investigated. Two variants concerning the muscle MUs structure are also compared: each muscle is composed of four distinct types MUs and the MUs twitch parameters are uniformly distributed. (C) Elsevier Ltd. All rights reserved.
Steam generation systems are a crucial part of most power plants. Therefore, boiler control is an important problem for power plants that are frequently changing load or subject to sudden load disturbances, which are ...
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ISBN:
(纸本)9781538650653
Steam generation systems are a crucial part of most power plants. Therefore, boiler control is an important problem for power plants that are frequently changing load or subject to sudden load disturbances, which are common in current market driven electricity industry. In this paper, the steam flow parameters of a boiler are controlled using fuzzy supervisory PID controller and then optimized using hierarchical genetic algorithm to find the best values of proportional gain (Kp), integral gain (KI), derivative gain (KD).
The need for efficient integration of an Electric Vehicles (EVs) public transportation system into Smart Grids (SGs), has sparked the idea to equip them with Renewable Energy Systems (RESs), in order to reduce their i...
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ISBN:
(纸本)9789897584756
The need for efficient integration of an Electric Vehicles (EVs) public transportation system into Smart Grids (SGs), has sparked the idea to equip them with Renewable Energy Systems (RESs), in order to reduce their impact on the SG. As a consequence, an EV can be seen as a Nanogrid (NG) whose energy flows are optimized by an Energy Management System (EMS). In this work, an EMS for an electric boat is synthesized by a Fuzzy Inference System-hierarchical genetic algorithm (FIS-HGA). The electric boat follows cyclic routes day by day. Thus, single day training and test sets with a very short time step are chosen, with the aim of reducing the computational cost, without affecting accuracy. A convex optimization algorithm is applied for benchmark tests. Results show that the EMS succesfully performs the EV energy flows optimization. It is remarkable that the EMS achieves good performances when tested on different days than the one it has been trained on, further reducing the computational cost.
The co-synthesis of hardware-software systems for complex embedded applications has been studied extensively with focus on various qualitative system objectives such as high speed performance and low power dissipation...
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The co-synthesis of hardware-software systems for complex embedded applications has been studied extensively with focus on various qualitative system objectives such as high speed performance and low power dissipation. One of the main challenges in the construction of multiprocessor systems for complex real time applications is provide high levels of system availability that satisfies the users' expectations. Even though the area of hardware software cosynthesis has been studied extensively in the recent past, the issues that specifically relate to design exploration for highly available architectures need to be addressed more systematically and in a manner that supports active user participation. In this paper, we propose a user-centric co-synthesis mechanism for generating gracefully degrading, heterogeneous multiprocessor architectures that fulfills the dual objectives of achieving real-time performance as well as ensuring high levels of system availability at acceptable cost. A flexible interface allows the user to specify rules that effectively capture the users' perceived availability expectations under different working conditions. We propose an algorithm to map these user requirements to the importance attached to the subset of services provided during any functional state. The system availability is evaluated on the basis of these user-driven importance values and a CTMC model of the underlying fail-repair process. We employ a stochastic timing model in which all the relevant performance parameters such as task execution times, data arrival times and data communication times are taken to be random variables. A stochastic scheduling algorithm assigns start and completion time distributions to tasks. A hierarchical genetic algorithm optimizes the selections of resources, i.e. processors and busses, and the task allocations. We report the results of a number of experiments performed with representative task graphs. Analysis shows that the co-synthesis tool we
The problem that how to use hierarchical genetic algorithm to determine the structure and parameters of neural networks was studied. Utilized the two grade coding structure of the hierarchical genetic algorithm to sol...
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The problem that how to use hierarchical genetic algorithm to determine the structure and parameters of neural networks was studied. Utilized the two grade coding structure of the hierarchical genetic algorithm to solve the ancient problem that when optimize the neural networks' structure, connection weights, threshold at the same time, the efficiency was low. Furthermore, an improved adaptive hierarchical genetic algorithm was educed, and it improved the shortage of the normal adaptive hierarchical genetic algorithm. At last, the improved adaptive geneticalgorithm is used to the fault diagnosis of three-phase inverter, the simulation result shown the method was correct and applied.
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